When COVID-19 strikes, we learn from the traces 

One of the major challenges in radiology is dealing with the large amounts of highly complex data generated during examinations. Comparatively few specialists can evaluate the data, and it is a time-consuming process. As a result, applications using artificial intelligence (AI) are becoming increasingly important. Even today, task-specific AI solutions can help physicians to detect patterns and recognize systematic phenomena in their investigations.

In the case of COVID-19, abnormal regions showing airway disease are localized and delineated in the CT image. Once an abnormal region has been detected in an image, the computer can learn from the examples and apply the extracted concepts to new data. The quality of the annotations and data has a major impact on the quality of the final product.

The world is currently in a state of emergency, with governments everywhere restricting personal contact. However, this does not prevent researchers worldwide from pioneering new methods and developing new ideas. Siemens Healthineers formed an international and interdisciplinary team comprising AI scientists from the U.S., software developers from India, CT product as well as research and development experts from Germany, and medical experts from around the globe – and brought them together around a virtual conference table, all with a common goal.

Team member Thomas Re, an experienced radiologist, remembers the situation very well: “As for everyone, it was an extremely intense moment. Having trained in radiology in Milan, Italy, a location particularly hit by COVID-19 infections and fatalities just before the U.S. outbreak, and having heard from friends and colleagues directly affected, I was particularly aware of the gravity of the situation. I saw my work on the AI COVID-19 project as my best opportunity to provide a solution in future that could help in this time of crisis by working to improve COVID-19 diagnostic tools for the clinicians on the font-line of the pandemic. My task, as a radiologist, was to annotate imaging data for use in “training” the AI system to recognize COVID-19 disease patterns in chest CT data. Pulling together resources from around the globe for this project and to be part of such a team was truly amazing. My contribution would not have been possible without the support of other local and international team members; in particular, that of my radiology colleague Eileen Krieg, MD based in New Jersey, engineer Amit Vaze and his India based team as well as engineer Guillaume Chabin and his data management team in Paris.”

Due to the global pandemic, the team was restricted to virtual collaboration in video conferences and used interactive platforms and teamworking tools. "Everyone always had the same level of knowledge," explains Georgescu. His colleague Shikha Chaganti remembers: “We've all been doing it with a smile on our faces. A wonderful sense of community.” This provided the best foundation for meeting the challenge of developing a working prototype at incredible speed, as radiologist Abishek Balachandran confirms: “It was amazing to collaborate on this project. Given that it happened at a time of unprecedented crisis, we’re proud that even in this difficult period our team was able to scale up.” In just three weeks, the first version of the prototype was ready. It would be used to detect and quantify conspicuous tissue structures in the lung on a CT scan.

In order to research the effects of COVID-19 on the lung, the AI team needed reliable comparative values as a solid basis for continuing their work. "When you launch a project like this, you need reliable data and advice from clinical experts right from the start. Only then can we develop the actual prototype. This requires effective algorithms and a powerful computer infrastructure. And finally, it must be possible to make the new concept available to several partners in a clinical setting so that they can test its practical suitability," explains Georgescu.

Therefore, to build, train, and deploy a machine learning model, the AI scientists and engineers worked hand in hand with dedicated clinical partners. “We have managed to engage and effectively work and collaborate across continents to have an efficient data processing pipeline,” says Georgescu.

His team drew on data from COVID-19 cases and additional studies from around 18 collaborating institutes in the U.S., Canada, and Europe – including Foch Hospital (France), Northwell Health (U.S) and Houston Methodist (U.S.), University Hospital Basel (Switzerland), and Vancouver General Hospital (Canada) – as reference values. 

At all these locations, the same measures of lesions, lungs, and lobes were acquired to “feed” the algorithm with relevant data.

The process that was set in motion by Georgescu and his team could become a model for many future developments in terms of its approach, pace, and guiding principles. The pandemic is not all that has spread in the last few months: Pioneering spirit, cross-border project work, and intelligent collaborations have also flourished. An algorithm is just one of the many results to have come to fruition – and it has been shown that, in the fight against COVID-19, hard work and creativity are also contagious.

The prototype was developed to automatically identify and quantify hyperdense lung regions in order to detect the affected area and then assess the severity of the inflamed tissue. This automated quantification of abnormalities associated with COVID-19 from noncontrast chest CT scans could help clinicians to evaluate the disease and to assess its severity and progression. However, the accuracy with which COVID-19 can be distinguished from other types of lung disease on CT images still varies.